Machine learning, types of machine learning, supervised, unsupervised and reinforcement machine learning

1. MV PADMAVATI
BHILAI INSTITUTE OF TECHNOLOGY, DURG, INDIA
MACHINE LEARNING
“Learning denotes changes in a system that ... enable a system to do the same task …
more efficiently the next time.” - Herbert Simon

2. WHAT IS MACHINE LEARNING
Arthur Samuel described it as: “The field of study that gives computers the
ability to learn from data without being explicitly programmed.”

3. MACHINE LEARNING
Machine learning is a scientific discipline that is concerned with the design and
development of algorithms that allow computers to learn based on data, such as
from sensor data or databases.
A major focus of machine learning research is to automatically learn to recognize
complex patterns and make intelligent decisions based on data .

4. KIND OF PROBLEMS WHERE MACHINE LEARNING IS APPLICABLE

5. Traditional Programming
Program
Data Output
ML
algorithm
Data
Model
(or)
Hypothesis
Data whose output is to be predicted
Predicted
Output
Machine Learning
Data will be divided into training and testing data.

6. Iris Dataset for Classification
Sepal. length Sepal. width Petal.length Petal.width Class label
5.1 3.5 1.4 0.2 Iris-setosa
4.9 3 1.4 0.2 Iris-setosa
4.7 3.2 1.3 0.2 Iris-setosa
4.6 3.1 1.5 0.2 Iris-setosa
5 3.6 1.4 0.2 Iris-setosa
7 3.2 4.7 1.4 Iris-versicolor
6.4 3.2 4.5 1.5 Iris-versicolor
6.9 3.1 4.9 1.5 Iris-versicolor
5.5 2.3 4 1.3 Iris-versicolor
6.5 2.8 4.6 1.5 Iris-versicolor
6.1 2.8 4 1.3 Iris-versicolor
6.3 2.5 4.9 1.5 Iris-versicolor
6.1 2.8 4.7 1.2 Iris-versicolor
6.4 2.9 4.3 1.3 Iris-versicolor
6.3 3.3 6 2.5 Iris-virginica
5.8 2.7 5.1 1.9 Iris-virginica
7.1 3 5.9 2.1 Iris-virginica
6.3 2.9 5.6 1.8 Iris-virginica
Data will be in the form of .csv, .json, .xml, excel file etc

7. Where can I get datasets?
• Kaggle Datasets - https://www.kaggle.com/datasets
• Amazon data sets - https://registry.opendata.aws/
• UCI Machine Learning Repository-
https://archive.ics.uci.edu/ml/datasets.html
Many more…..
Prepare your Datasets OR you can get data from

8. Machine Learning Steps

9. Machine Learning Tools
• Git and Github
• Python
• Jupyter Notebooks
• Numpy - is mostly used to perform math based operations
during the machine learning process.
• Pandas - to import datasets and manage them
• Matplotlib - We will use this library to plot charts in python.
• scikit-learn is an open source Python machine learning library
• Many other Python APIs

10. Python for Machine Learning

11. Types of Machine Learning
• Supervised (labeled examples)
• Unsupervised (unlabeled examples)
• Reinforcement (reward)- Selects actions and observes consequences.

12. Supervised learning
•Machine learning takes data as input. lets call this data Training data
•The training data includes both Inputs and Labels(Targets)
•We first train the model with the lots of training data(inputs & targets)

13. Types of Supervised learning
Classification separates the data, Regression fits the data

14. Basic Problem: Induce a representation of a function (a systematic relationship between
inputs and outputs) from examples.
 target function f: X → Y
 example (x, f(x))
 hypothesis g: X → Y such that g(x) = f(x)
x = set of attribute values (attribute-value representation)
Y = set of discrete labels (classification)
Y = continuous values  (regression)
Inductive (Supervised) Learning

15. Classification
This is a type of problem where we predict the categorical response value where the data can be
separated into specific “classes” (ex: we predict one of the values in a set of values).
Some examples are :
1. This mail is spam or not?
2. Will it rain today or not?
3. Is this picture a cat or not?
Basically ‘Yes/No’ type questions called binary classification.
Other examples are :
1. This mail is spam or important or promotion?
2. Is this picture a cat or a dog or a tiger?
This type is called multi-class classification.

16. Iris Flower - 3 Variety Details
Let us first understand the datasets
The data set consists of: 150 samples
3 class labels: species of Iris (Iris setosa, Iris virginica and Iris versicolor)
4 features: Sepal length, Sepal width, Petal length, Petal Width in cm

17. Iris Dataset for Classification
Sepal. length Sepal. width Petal.length Petal.width Species
5.1 3.5 1.4 0.2 Iris-setosa
4.9 3 1.4 0.2 Iris-setosa
4.7 3.2 1.3 0.2 Iris-setosa
4.6 3.1 1.5 0.2 Iris-setosa
5 3.6 1.4 0.2 Iris-setosa
7 3.2 4.7 1.4 Iris-versicolor
6.4 3.2 4.5 1.5 Iris-versicolor
6.9 3.1 4.9 1.5 Iris-versicolor
5.5 2.3 4 1.3 Iris-versicolor
6.5 2.8 4.6 1.5 Iris-versicolor
6.1 2.8 4 1.3 Iris-versicolor
6.3 2.5 4.9 1.5 Iris-versicolor
6.1 2.8 4.7 1.2 Iris-versicolor
6.4 2.9 4.3 1.3 Iris-versicolor
6.3 3.3 6 2.5 Iris-virginica
5.8 2.7 5.1 1.9 Iris-virginica
7.1 3 5.9 2.1 Iris-virginica
6.3 2.9 5.6 1.8 Iris-virginica

18. Regression
This is a type of problem where we need to predict the continuous response value (ex : above we
predict number which can vary from infinity to +infinity)
Some examples are
1. What is the price of house in Durg?
2. What is the value of the stock?
3. What can the temperature tomorrow?
etc… there are tons of things we can predict if we wish.

19. Predicting Age- Regression Problem

20. Unsupervised Learning
The training data does not include Targets here so we don’t tell the system where to go, the
system has to understand itself from the data we give.

21. Clustering
This is a type of problem where we group similar things together. It is similar to multi class classification but here we
don’t provide the labels, the system understands from data itself and cluster the data.
Some examples are :
1. Given news articles, cluster into different types of news
2. Given a set of tweets, cluster based on content of tweet
3. Given a set of images, cluster them into different objects

23. You’re running a company, and you want to develop learning algorithms to address each of two problems.
Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over
the next 3 months.
Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been
hacked or not.
Should you treat these as classification or as regression problems?
Treat both as classification problems.
Treat problem 1 as a classification problem, problem 2 as a regression problem.
Treat problem 1 as a regression problem, problem 2 as a classification problem.
Treat both as regression problems.

24. Of the following examples, which learning you make use of
3. Given a database of customer data, automatically discover market
segments and group customers into different market segments.
1. Given email labeled as spam/not spam, learn a spam filter.
2. Given a set of news articles found on the web, group them into set of
articles about the same story.
4. Given a dataset of patients diagnosed as either having diabetes or not,
learn to classify new patients as having diabetes or not.
Ans 1: Supervised Learning - Classification
Ans 2: Unsupervised Learning - Clustering
Ans 3: Unsupervised Learning - Clustering
Ans 4: Supervised Learning - Classification

25. Reinforcement learning
Close to human learning.
• Algorithm learns a policy of how to act in a given environment.
• Every action has some impact in the environment, and the
environment provides rewards that guides the learning
algorithm.

26. Reinforcement learning
Examples:
• A robot cleaning my room and recharging its battery
• Robot-soccer
• How to invest in shares
• Modeling the economy through rational agents
• Learning how to fly a helicopter
• Scheduling planes to their destinations

27. Reinforcement learning
Meaning of Reinforcement:
Occurrence of an event, in the proper relation to a response, that tends to increase
the probability that the response will occur again in the same situation.
Reinforcement learning is the problem faced by an
• agent that learns behavior through trial-and-error interactions with a dynamic
environment.
• Reinforcement Learning is learning how to act in order to maximize a numerical
reward.

28. Elements of Reinforcement learning

29. Reinforcement learning